Python for Deep Learning: Build Neural Networks in Python

Explore how deep learning uses artificial neural networks, brain-inspired, as densely linked neurons form computational models that mimic nerve cells and drive fundamental algorithms in deep learning.

Explore how billions of neurons transmit electrical impulses across the nervous system, connecting brain, skin, glands, muscles, and organs to enable movement and sensation.

Explore how artificial neurons mirror biological neurons by receiving inputs and transferring signals through synapses, using transfer and activation functions. Learn how gradient descent and backpropagation adjust weights to learn.

Explore feed-forward networks where data moves from input to output without cycles, and backpropagation, where weights are adjusted by moving backward to improve results.

Compute the output-layer error by comparing predicted and actual values, then backpropagate to adjust weights toward minimum error, highlighting how the network contributes to the error.

Tune network weights with back propagation to minimize the error between predicted and actual values, reducing the cost function and improving the accuracy of neural networks.

Explore neural network architectures in deep learning through the single layer perceptron, the fundamental unit with input and output and no hidden layer, before multilayer networks.

Explain the radial basis network, a feedforward model with input, hidden layer, and output, using the radial basis function as activation to assign an input value and an absolute value.

Explore multilayer perceptron neural networks, deep feedforward nets with an input layer, an output layer, and multiple hidden layers—the two hidden layers drive computation—then transition to the recurrent neural network.

Explore long short term memory networks, a recurrent neural network with memory cells that learn long-term dependencies and avoid long-term dependency issues.

Explore the Hopfield neural network, a fully interconnected recurrent network with equal numbers of neurons, trained by setting neuron values to desired patterns and computing weights via deep learning algorithms.

Explore the Boltzmann machine neural network, a recurrent network with neurons connected to inputs, others not, using initialized weights learned via back propagation to yield binary judgments predisposed by biases.

Explore activation functions, or transfer functions, and how they let neural networks learn complex patterns by converting prior neuron outputs through nonlinear functions to the next layer.

Explain key terminologies for non-linear activation functions, including differential function (the slope) and monotonic function (strictly increasing or decreasing), and begin exploring activation functions.

Explore the hyperbolic tangent function, with a -1 to 1 range, its differential and monotonic properties, advantages for negative inputs, and its use in feed-forward propagation for binary classification.

Explore softmax as the activation for the output layer, applying exponentials to all values, normalizing by their sum to produce probabilities for multiclass classification, and noting it is differentiable.

ReLU maps negative inputs to zero and passes through range from zero to infinity, commonly used in cnns to avoid vanishing gradients, but is not monotonic and can destabilize learning.

Explore the leaky rectified linear unit, a modification of ReLU offsetting its negative impact. State that alpha is about 0.01; it ranges from negative to positive infinity, differentiable and monotonic.

Learn how gradient descent updates model parameters to minimize a cost function, starting from initial values and moving toward the minimum with a step size governed by gamma.

Explore why gradient descent struggles with non-convex shapes and local minima, and how stochastic gradient descent uses random single-sample gradients for faster training and noisy but efficient paths.

Gradient descent uses whole dataset per iteration to measure gradient and update cost function parameters. Stochastic gradient descent uses a single value or subset for faster updates on large data.

Explore how neural networks use neurons in input, hidden, and output layers with weights and sigmoid activation, where a hidden layer adds flexibility to improve accuracy.

Discover how artificial neural networks handle incomplete knowledge after training and still produce outputs, with performance depending on missing information, while benefiting from fault tolerance and parallel processing.

Explore the disadvantages of artificial neural networks, including hardware dependency on parallel processing power and the lack of rules for selecting network structure, which requires experience and trial and error.

Explore practical applications of neural networks across real-world tasks, including handwritten recognition, image compression, and stock exchange prediction, powered by deep learning with Python.

Implement artificial neural networks in Python, exploring classification and regression with the churn modeling dataset from Kaggle.

Explore the churn dataset and its features, including customer id, geography, gender, age, tenure, balance, number of products, credit card status, active member, salary, and the target variable exited.

Analyze bank customer behavior using independent variables to determine if customers leave or stay, and build a predictive model from data set to predict whether new customers stay or leave.

We import the data set with Read Underscore 6V from the Panda's library, noting 13 independent variables and one dependent variable, eliminate customer ID and surname, creating X and Y.

Split the dataset into independent variable X from column 3:13 and dependent variable Y from column 13 using iloc, then print X and Y to verify the split.

Transform gender and geography attributes into numeric form using label encoding and one hot encoding with scikit-learn pre-processing, enabling robust numeric input for deep learning models.

Learn to implement one-hot encoding in Python using scikit-learn's column transformer and one-hot encoder, transforming categorical geography data into numerical features for neural networks.

Split data into training and test sets, using 80–90 percent for training, to train a model and validate predictions. Use train_test_split with test_size 0.2 and random_state for reproducible splits.

learn how feature scaling standardizes data with the standard scaler from sklearn, fitting on training data and transforming both train and test sets to a fixed range.

Move from preprocessing to building an artificial neural network using Kara's library carers, open-source python toolkit for deep learning, and initialize a sequential classifier to create a fully connected network.

Add a dense layer to the classifier with six units and 11 input features, using a uniform weight initializer and relu activation to form the first hidden layer.

Add a second hidden layer with six units, uniformly initialized and using a rectifier activation, auto-connected to the previous layer, following one input layer and two hidden layers.

Add the output layer with a single dense unit and sigmoid activation to predict a binary outcome, providing the probability of a customer leaving or staying.

Compile the artificial neural network with the Adam optimizer, binary cross-entropy loss, and accuracy metrics to prepare it for training with your data.

Fit the model with the fit method using x_train, y_train, batch_size=10, and epochs=100; monitor accuracy as it rises from about 80% to 85.44%, then predict on the test set.

Predict test data using the classifier, apply a 0.5 threshold to obtain binary y_pred, and evaluate with a confusion matrix and 85.75% accuracy.

Explore convolutional neural networks (CNNs) for image analysis, learn how large data and computing power revived CNNs in 2012, and how image normalization scales pixels from zero to one.

Explore the two-step workflow of convolutional neural networks: feature extraction with filters and activation, then classification. Observe a convolutional neural network layout with input layer, pooling, and fully connected layers.

A convolution layer applies a filter to an input image to extract features and produce a feature map; the filter slides with a stride, followed by an activation function.

Apply the pooling layer after the convolutional layer to reduce feature map dimensions and learnable parameters while preserving key information, using max pooling and average pooling.

Explore how to transition from convolution and pooling to fully connected layers by flattening the 2d feature maps into a 1d vector, enabling image classification.

Implement convolutional neural networks (CNNs) in Python using the MNIST handwritten digit dataset, noting that images are pre-aligned, contain a single grayscale digit, and are 28 by 28 pixels.

Import the required libraries in a Jupyter notebook to build a CNN for two dimensional images, using the sequential model and 2d convolution, max pooling, flatten, and dropout.

Load and preprocess the dataset, reshape and normalize 28x28 grayscale images, then build and train a CNN with conv layers, pooling, flatten, and dense output for 10 digits using softmax.

Evaluate the trained CNN model on test data using model.evaluate, achieving an accuracy of 98.64 percent, marking the completion of building your first CNN model.